Cancer is typically diagnosed by analyzing stained samples of tissue from cancer patients and then correlating target patterns in the tissue samples with grading and scoring methods for different kinds of cancers. For example, the Gleason grading system indicates the malignancy of prostate cancer based on the architectural pattern of the glands of a stained prostate tumor. The Fuhrman nuclear grading system indicates the severity of renal cell carcinoma (RCC) based on the morphology of the nuclei of kidney cells. Breast cancer can be diagnosed by grading stained breast tissue using the Allred score, the the Elston-Ellis score, the HercepTest® score or the Ki-67 test score. The Allred score indicates the severity of cancer based on the percentage of cells that have been stained to a certain intensity by the estrogen receptor (ER) antibody. The Elston-Ellis score indicates the severity of cancer based on the proportion of tubules in the tissue sample, the similarity of nucleus sizes and the number of dividing cells per high power field of 40× magnification. The HercepTest score indicates the severity of cancer based on the level of HER2 protein overexpresssion as indicated by the degree of membrane staining. The Ki-67 test measures the proliferation rate, which is the percentage of cancer cells in the breast tissue that are actively dividing. The Ki-67 labeling index is a measure of the percentage of cancer cells whose nuclei contain the Ki-67 protein that has been immunohistochemically stained. A level of greater than twenty percent indicates a high-risk, aggressive tumor.
The accuracy of these scoring and grading systems depends, however, on the accuracy of the image analysis of the stained tissue. Human error is one cause of inconsistent scoring that results when a human operator, such as a pathologist, misjudges the target patterns and structures in the stained tissue due to fatigue or loss of concentration. Computer-assisted image analysis systems have been developed to support pathologists in the tedious task of grading and scoring digital images of stained tissue samples. But even the accuracy of computer-assisted scoring methods is limited by the quality of the digital images of the stained tissue. One cause of inaccuracy in scoring occurs when image analysis is performed on blurred areas of digital images of tissue slices. Conventionally, the pathologist manually marks the blurred areas of the image of each tissue slice that are to be avoided when performing the object and pattern recognition that is the basis for the diagnostic cancer scoring. However, the pathologist can only mark large blurred areas, such as a scanning stripe along the entire slide that is out of focus, as opposed to the thousands of smaller blurred areas in a high resolution image that can result from the differing light refraction caused by microdroplets on the tissue.
A method is sought to identify and mark the many small blurred areas in digital images of tissue slices so as to improve the accuracy of cancer scoring by using image analysis results from only unblurred areas.